The ability to forecast monsoon rains well in advance has long eluded meteorologists. But if the new approach proves successful, the bureau thinks it could lead to better summertime projections of Lake Mead’s water level in January – a key metric the agency uses to plan water releases in the course of the coming year. With water levels in Lake Mead reaching an all-time high and the bureau implementing its first-ever water shortage declaration in 2022, even small improvements in these reservoir projections can make a difference.
These maps illustrate the severity of the western drought
The monsoon forecast tool, detailed in a recent study published in the journal Advancing Earth, Space and Science, is one of many emerging efforts by researchers to predict key weather patterns, or weather-related hazards, months in advance. Other research teams are also deploying experimental long-range forecasts that can predict how many acres will burn over the next forest fire season and or ocean heat waves will take place up to a year in advance, providing more explicit and comprehensive information than existing tools. Together, these new tools push the boundaries of seasonal forecasting, an area of weather forecasting that has traditionally focused on predicting temperature and precipitation patterns.
“When we think about very long lead times and the ability of models to predict if there is moisture available and if the atmospheric models are in the right configuration, going beyond this traditional approach of looking myopically at temperature and precipitation is the way to go,” said Chris Castroa North American monsoon expert at the University of Arizona who was not involved in the new monsoon paper.
Runoff from the snowpack in the Rocky Mountains provides most of the water to the reservoirs that supply the southwest. But as the drought persists, water managers are increasingly concerned about the rain that falls on Arizona and New Mexico in the summer due to the monsoon, a seasonal weather pattern. But monsoon rainfall, which falls in bursts as thunderstorms quickly form and dissipate over the mountains, is notoriously difficult to predict with a lead time of just a few days.
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“Our modeling systems are very bad at simulating this type of thunderstorm precipitation, especially in areas where you have mountains like the desert southwest,” said Andreas Prein of the National Center for Atmospheric Research, lead author of the recent study.
To try to predict rainfall further in advance, Prein and his colleagues turned to a weather model from the European Center for Medium-Range Weather Forecasts. This model, the researchers found, can reliably simulate large-scale atmospheric humidity surges from the Gulf of Mexico or the tropical Pacific over the southwestern United States several months in advance. These bursts of moisture correlate with the amount of rain that actually falls, in a given month, on a given drainage area. This allows researchers to predict how much it will rain, although they cannot say exactly where or when thunderstorms will form.
As the Bureau of Reclamation puts this new tool to the test this summer, a separate research team with the National Oceanic and Atmospheric Administration is developing seasonal forecasts for a climate hazard far offshore: ocean heat waves.
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Ocean heat waves, which like their terrestrial counterparts get worse as the planet warms, can impact everything from commercial fishing productivity to migration patterns of endangered species. The ability to predict them well in advance would help resource managers prepare for these impacts – which is exactly what NOAA scientists say are now possible with their new seasonal forecasting tool.
“The high-level conclusion is that in many cases these events are predictable,” said Michael Jacoban oceanographer at NOAA’s Southwest Fisheries Science Center in Monterey, California, who led the to research recently published in the journal Nature.
Jacox and his colleagues compared 30 years of forecasts produced by six North American climate models with actual measurements of sea surface temperature collected at the same time, and found specific ocean regions where heat waves are predictable from months in advance. These include the eastern tropical Pacific, where heat waves are closely linked to the El Niño climate model and are predictable nearly a year in advance, and the west coast of North America, where forecasts are possible up to six months in advance. In general, ocean regions influenced by El Niño and La Niña have predictable multi-month heat wave patterns, while the western edges of the oceans that are more influenced by rapidly changing currents do not have such patterns. according to research.
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For predictable ocean regions, the authors believe that a heat wave forecasting system could be easily developed because the underlying climate models are already run monthly.
Ocean managers are eager to see this system built.
Steven Lonhart, a research ecologist at the Monterey Bay National Marine Sanctuary, said long-term forecasts of heat waves could help natural resource agencies monitor the health of species critical to their environment, like kelp, and prepare. help heat-vulnerable animals like sea otters. Fisheries managers, meanwhile, could use heat wave forecasts to modify annual catch quotas or the timing of the fishing season to account for the impact of warm water on the population.
Wildfires, another hazard that is intensifying across the country, are also becoming more predictable on seasonal timescales. A recent study published in Environmental Research Letters found that winter and spring weather conditions contribute to the severity of the western summer fire season, which explains more than half of the variability and trend of a year to year of the area burned in the summer.
“What we found is that the winter and spring climate sets the stage for fire activity in the summer,” said Ronnie Abolafia-Rosenzweig, lead study author and NCAR scientist.
With this idea in mind, Abolafia-Rosenzweig and his colleagues developed an experimental tool to predict, at the end of April, the amount of land in the West that will burn between June and September. The researchers feed data on precipitation, temperature, atmospheric humidity and other climate parameters from the previous winter and spring into the tool, which is based on machine learning models. Based on these conditions, models predict how much land will burn in a given summer, a prediction researchers can validate by comparing it to the burned area measured by satellite.
This summer, their models predict that 3.8 million acres of land will burn, a prediction the researchers will validate as the summer progresses. Although still experimental, such predictions go beyond fire season operational outlookwhich focus on whether fire activity in the West will be above or below average.
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John Abatzoglou, a fire expert at the University of California, Merced, said the authors’ findings “add another tool to the toolbox” to help fire managers take preventative action ahead of fire season. But Abatzoglou, who did not participate in the article, said summer weather is of “paramount importance” to the severity of the fire season – conditions that are not currently factored into the tool.
Study co-author Cenlin He acknowledged that forecasting and integrating summer weather, as well as summer activities that cause fires, into seasonal forecasts is a “very difficult problem and different groups of the scientific community are working to solve this problem”.
All of these seasonal advances in forecasting could benefit communities affected by extreme weather and climate – although that’s not the same as being able to tell exactly when and where a specific weather event or hazard will occur with months. notice.
“The state of the science isn’t there yet,” said Jon Gottschalck, who leads the operational forecasting branch at NOAA’s Climate Prediction Center.
Still, Gottschalck said the kinds of approaches the new studies take — data mining with machine learning; predicting large-scale patterns that correlate with specific weather types – could be important in advancing broader seasonal forecasting, with many potential applications.
For example, the authors of the monsoon study are examining whether their approach can be used to predict winter snowfall on the Rockies several months in advance. According to office hydrologist and study co-author Shana Tighi, winter snow accumulation is “the main factor” in annual runoff and water supply forecasts.
“Any tool that improves snow accumulation forecasts will be extremely useful for water management planning,” Tighi said.